21 papers · ranked by Valyu relevance
Asifullah Khan, Anabia Sohail, Umme Zahoora, Aqsa Saeed Qureshi
Deep Convolutional Neural Network (CNN) is a special type of Neural Networks, which has shown exemplary performance on several competitions related to Computer Vision and Image Processing. Some of the exciting application areas of CNN include Image Classification and Segmentation, Object Detection, Video Processing…
Shengli Jiang, Víctor M. Zavala
In this paper we review the mathematical foundations of convolutional neural nets (CNNs) with the goals of: i) highlighting connections with techniques from statistics, signal processing, linear algebra, differential equations, and optimization, ii) demystifying underlying computations, and iii) identifying new types…
Ujjwal Thakur, Anuj Sharma
The Training of convolutional neural networks is a high-dimensional and non-convex optimization problem. At present, it is inefficient in situations where parametric learning rates can not be confidently set. Some past works have introduced Newton's methods for training deep neural networks. Newton's methods for…
Rezoana Bente Arif, Md. Abu Bakr Siddique, Mohammad Mahmudur Rahman Khan, Mahjabin Oishe
'Mohammad Mahmudur Rahman Khan' 'Mahjabin Oishe'] Abstract— Nowadays, deep learning can be employed to a wide ranges of fields including medicine, engineering, etc. In deep learning, Convolutional Neural Network (CNN) is extensively used in the pattern and sequence recognition, video analysis, natural language…
Serkan Kıranyaz, Onur Avcı, Osama Abdeljaber, Türker İnce + 2 more
'Moncef Gabbouj' 'Daniel Inman'] During the last decade, Convolutional Neural Networks (CNNs) have become the de facto standard for various Computer Vision and Machine Learning operations. CNNs are feed-forward Artificial Neural Networks (ANNs) with alternating convolutional and subsampling layers. Deep 2D CNNs with…
Authors not listed
This research presents a novel approach to obstacle detection during navigation using a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The primary objective is to generate accurate image captions that describe the content of images, which is crucial for applications such…
Yaoda Xu, Maryam Vaziri-Pashkam
Convolutional neural networks (CNNs) have achieved very high object categorization performance recently. It has increasingly become a common practice in human fMRI research to regard CNNs as working model of the human visual system. Here we reevaluate this approach by comparing fMRI responses from the human brain in…
Sanket Kadulkar, Michael Howard, Thomas Truskett, Venkat Ganesan
We develop a convolutional neural network (CNN) model to predict the diffusivity of cations in nanoparticle-based electrolytes, and use it to identify the characteristics of morphologies which exhibit optimal transport properties. The ground truth data is obtained from kinetic Monte Carlo (kMC) simulations of cation…
Authors not listed
The automatic generation of image captions in natural language is a critical and challenging task, particularly in the context of environmental monitoring and control. This paper presents a novel deep learning-driven image captioning system designed for real-time monitoring and predictive control of pollutant gas…
Christian Tsvetkov, Gaurav Malhotra, Benjamin D. Evans, Jeffrey S. Bowers
Convolutional neural networks (CNNs) are often described as promising models of human vision, yet they show many differences from human abilities. We focus on a superhuman capacity of top-performing CNNs, namely, their ability to learn very large datasets of random patterns. We verify that human learning on such tasks…
Michelle R. Greene, Bruce C. Hansen
Understanding the computational transformations that enable invariant visual categorization is a fundamental challenge in both systems and cognitive neuroscience. Recently developed deep convolutional neural networks (CNNs) perform visual categorization at accuracies that rival humans, providing neuroscientists with…
Chen-Ping Yu, Huidong Liu, Dimitris Samaras, Gregory Zelinsky
Recently we proposed that people represent object categories using category-consistent features (CCFs), those features that occur both frequently and consistently across a categorys exemplars [70]. Here we designed a Convolutional Neural Network (CNN) after the primate ventral stream (VsNet) and used it to extract CCFs…
Kandan Ramakrishnan, Iris I.A. Groen, Arnold W.M. Smeulders, H. Steven Scholte + 1 more
Convolutional neural networks (CNNs) have recently emerged as promising models of human vision based on their ability to predict hemodynamic brain responses to visual stimuli measured with functional magnetic resonance imaging (fMRI). However, the degree to which CNNs can predict temporal dynamics of visual object…
Andrew McNutt, Yanjing Li, Paul Francoeur, David Koes
Knowledge of the bound protein-ligand structure is critical to many drug discovery tasks. One tool for in silico bound structure elucidation is molecular docking, which samples and scores ligand binding conformations. Recent work has demonstrated that convolutional neural networks (CNNs) for protein-ligand pose scoring…
Pinaki Saha, Minh Tho Nguyen
Determination and prediction of atomic cluster structures is an important endeavor in the field of nanoclusters and thereby in materials research. To a large extent the fundamental properties of a nanocluster including its chemical, optical, magnetic, mechanical and transport properties are mainly governed by the…
Tyler Bradshaw, Alan B. McMillan
— The influence of artificial intelligence (AI) within the field of nuclear medicine has been rapidly growing. Many researchers and clinicians are seeking to apply AI within PET, and clinicians will soon find themselves engaging with AI-based applications all along the chain of molecular imaging, from image…
Matteo Pinna, Léo Picard, Christoph Goessmann
COVID-19 vaccines have reduced infections and hospitalizations across the globe, yet resistance to vaccination remains strong. This paper investigates the role of cable television news in vaccine hesitancy and associated local vaccination rates in the United States. We find that, in the earlier stages of the vaccine…
Jean Mathieu Tsoumou
Impoliteness in the Coronavirus Pandemic Era Authors: ['Jean Mathieu Tsoumou'] Whilst the coronavirus pandemic keeps threatening the world at large, little is yet known about the impoliteness implications of user-generated- Covid-related contents on social media such as Facebook. The aim of this study is to examine the…
Homa Hosseinmardi, Samuel Wolken, David M. Rothschild, Duncan J. Watts
'Duncan J. Watts'] The potential for a large, diverse population to coexist peacefully is thought to depend on the existence of a public sphere in which citizens are exposed to similar facts about similar topics. A generation ago, broadcast television news was widely considered to serve this function; however, since…
Authors not listed
Predicting protein-ligand binding affinity from three-dimensional (3D) structural data is a central task in structure-based drug discovery, yet it remains challenging due to limited data availability, structural complexity, and the sparse nature of 3D molecular representations. In this study, we investigate the…
Johann D. Gaebler, Sean J. Westwood, Shanto Iyengar, Sharad Goel + 1 more
Despite the rise of digital media, Americans are five times more likely to consume news via television than through online platforms. However, due in large part to technical hurdles, it remains unclear what content appears on broadcast news and how the mixture of content has changed over time. We consider these…